Utilities and options for emmeans

Contents

1. Updating an emmGrid object
2. Setting options
   1. Setting and viewing defaults
   2. Optimal digits to display
   3. Startup options
3. Combining and subsetting emmGrid objects
4. Accessing results to use elsewhere
5. Adding grouping factors
6. Re-labeling and re-leveling an emmGrid

Index of all vignette topics

Updating an emmGrid object

Several internal settings are saved when functions like ref_grid(), emmeans(), contrast(), etc. are run. Those settings can be manipulated via the update() method for emmGrids. To illustrate, consider the pigs dataset and model yet again:

pigs.lm <- lm(log(conc) ~ source + factor(percent), data = pigs)
pigs.emm <- emmeans(pigs.lm, "source")
pigs.emm

##  source emmean     SE df lower.CL upper.CL
##  fish     3.39 0.0367 23     3.32     3.47
##  soy      3.67 0.0374 23     3.59     3.74
##  skim     3.80 0.0394 23     3.72     3.88
## 
## Results are averaged over the levels of: percent 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95

We see confidence intervals but not tests, by default. This happens as a result of internal settings in pigs.emm.s that are passed to summary() when the object is displayed. If we are going to work with this object a lot, we might want to change its internal settings rather than having to rely on explicitly calling summary() with several arguments. If so, just update the internal settings to what is desired; for example:

pigs.emm.s <- update(pigs.emm, infer = c(TRUE, TRUE), null = log(35),
                     calc = c(n = ".wgt."))
pigs.emm.s

##  source emmean     SE df  n lower.CL upper.CL null t.ratio p.value
##  fish     3.39 0.0367 23 10     3.32     3.47 3.56  -4.385  0.0002
##  soy      3.67 0.0374 23 10     3.59     3.74 3.56   2.988  0.0066
##  skim     3.80 0.0394 23  9     3.72     3.88 3.56   6.130  <.0001
## 
## Results are averaged over the levels of: percent 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95

Note that by adding of calc, we have set a default to calculate and display the sample size when the object is summarized. See help("update.emmGrid") for details on the keywords that can be changed. Mostly, they are the same as the names of arguments in the functions that construct these objects.

Of course, we can always get what we want via calls to test(), confint() or summary() with appropriate arguments. But the update() function is more useful in sophisticated manipulations of objects, or called implicitly via the ... or options argument in emmeans() and other functions. Those options are passed to update() just before the object is returned. For example, we could have done the above update within the emmeans() call as follows (results are not shown because they are the same as before):

emmeans(pigs.lm, "source", infer = c(TRUE, TRUE), null = log(35),
        calc = c(n = ".wgt."))

Back to contents

Setting options

Speaking of the options argument, note that the default in emmeans() is options = get_emm_option("emmeans"). Let’s see what that is:

get_emm_option("emmeans")

## $infer
## [1]  TRUE FALSE

So, by default, confidence intervals, but not tests, are displayed when the result is summarized. The reverse is true for results of contrast() (and also the default for pairs() which calls contrast()):

get_emm_option("contrast")

## $infer
## [1] FALSE  TRUE

There are also defaults for a newly constructed reference grid:

get_emm_option("ref_grid")

## $is.new.rg
## [1] TRUE
## 
## $infer
## [1] FALSE FALSE

The default is to display neither intervals nor tests when summarizing. In addition, the flag is.new.rg is set to TRUE, and that is why one sees a str() listing rather than a summary as the default when the object is simply shown by typing its name at the console.

emm_options(emmeans = list(type = "response"),
            contrast = list(infer = c(TRUE, TRUE)))

pigs.anal.p <- emmeans(pigs.lm, consec ~ percent)
pigs.anal.p

## $emmeans
##  percent response   SE df lower.CL upper.CL
##        9     31.4 1.28 23     28.8     34.1
##       12     37.5 1.44 23     34.7     40.6
##       15     39.0 1.70 23     35.6     42.7
##       18     42.3 2.24 23     37.9     47.2
## 
## Results are averaged over the levels of: source 
## Confidence level used: 0.95 
## Intervals are back-transformed from the log scale 
## 
## $contrasts
##  contrast              ratio     SE df lower.CL upper.CL null t.ratio p.value
##  percent12 / percent9   1.20 0.0671 23    1.038     1.38    1   3.202  0.0112
##  percent15 / percent12  1.04 0.0604 23    0.896     1.20    1   0.650  0.8613
##  percent18 / percent15  1.09 0.0750 23    0.911     1.29    1   1.194  0.5202
## 
## Results are averaged over the levels of: source 
## Confidence level used: 0.95 
## Conf-level adjustment: mvt method for 3 estimates 
## Intervals are back-transformed from the log scale 
## P value adjustment: mvt method for 3 tests 
## Tests are performed on the log scale

options(emmeans = NULL)

emm_options(opt.digits = FALSE)
pigs.emm

##  source   emmean         SE df lower.CL upper.CL
##  fish   3.394492 0.03668122 23 3.318612 3.470373
##  soy    3.667260 0.03744798 23 3.589793 3.744727
##  skim   3.796770 0.03938283 23 3.715300 3.878240
## 
## Results are averaged over the levels of: percent 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95

emm_options(opt.digits = TRUE)  # revert to optimal digits

options(emmeans = list(lmer.df = "satterthwaite", 
                       contrast = list(infer = c(TRUE, FALSE))))

Combining and subsetting emmGrid objects

Two or more emmGrid objects may be combined using the rbind() or + methods. The most common reason (or perhaps the only good reason) to do this is to combine EMMs or contrasts into one family for purposes of applying a multiplicity adjustment to tests or intervals. A user may want to combine the three pairwise comparisons of sources with the three comparisons above of consecutive percents into a single family of six tests with a suitable multiplicity adjustment. This is done quite simply:

rbind(pairs(pigs.emm.s), pigs.anal.p[[2]])

##  contrast              estimate     SE df t.ratio p.value
##  fish - soy             -0.2728 0.0529 23  -5.153  0.0002
##  fish - skim            -0.4023 0.0542 23  -7.428  <.0001
##  soy - skim             -0.1295 0.0530 23  -2.442  0.1364
##  percent12 - percent9    0.1796 0.0561 23   3.202  0.0238
##  percent15 - percent12   0.0378 0.0582 23   0.650  1.0000
##  percent18 - percent15   0.0825 0.0691 23   1.194  1.0000
## 
## Results are averaged over some or all of the levels of: percent, source 
## Results are given on the log (not the response) scale. 
## P value adjustment: bonferroni method for 6 tests

The default adjustment is "bonferroni"; we could have specified something different via the adjust argument. An equivalent way to combine emmGrids is via the addition operator. Any options may be provided by update(). Below, we combine the same results into a family but ask for the “exact” multiplicity adjustment.

update(pigs.anal.p[[2]] + pairs(pigs.emm.s), adjust = "mvt")

##  contrast              ratio     SE df lower.CL upper.CL null t.ratio p.value
##  percent12 / percent9  1.197 0.0671 23    1.022    1.402    1   3.202  0.0214
##  percent15 / percent12 1.039 0.0604 23    0.881    1.224    1   0.650  0.9681
##  percent18 / percent15 1.086 0.0750 23    0.894    1.320    1   1.194  0.7305
##  fish / soy            0.761 0.0403 23    0.656    0.884    1  -5.153  0.0002
##  fish / skim           0.669 0.0362 23    0.574    0.779    1  -7.428  <.0001
##  soy / skim            0.879 0.0466 23    0.756    1.020    1  -2.442  0.1109
## 
## Results are averaged over some or all of the levels of: source, percent 
## Confidence level used: 0.95 
## Conf-level adjustment: mvt method for 6 estimates 
## Intervals are back-transformed from the log scale 
## P value adjustment: mvt method for 6 tests 
## Tests are performed on the log scale

Also evident in comparing these results is that settings are obtained from the first object combined. So in the second output, where they are combined in reverse order, we get both confidence intervals and tests, and transformation to the response scale.

pigs.emm[2:3]

##  source emmean     SE df lower.CL upper.CL
##  soy      3.67 0.0374 23     3.59     3.74
##  skim     3.80 0.0394 23     3.72     3.88
## 
## Results are averaged over the levels of: percent 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95

Accessing results to use elsewhere

Sometimes, users want to use the results of an analysis (say, an emmeans() call) in other computations. The summary() method creates a summary_emm object that inherits from the data.frame class; so one may use the variables therein just as those in a data frame.

An emmGrid object has its own internal structure and we can’t directly access the values we see displayed. If follow-up computations are needed, use summary() (or confint() or test()), creates a summary_emm object which inherits from data.frame – making it possible to access the values. For illustration, let’s add the widths of the confidence intervals in our example.

CIs <- confint(pigs.emm)
CIs$CI.width <- with(CIs, upper.CL - lower.CL)
CIs

##  source emmean     SE df lower.CL upper.CL CI.width
##  fish     3.39 0.0367 23     3.32     3.47    0.152
##  soy      3.67 0.0374 23     3.59     3.74    0.155
##  skim     3.80 0.0394 23     3.72     3.88    0.163
## 
## Results are averaged over the levels of: percent 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95

By the way, the values stored internally are kept to full precision, more than is typically displayed:

CIs$emmean

## [1] 3.394492 3.667260 3.796770

If you want to display more digits, specify so using the print method:

print(CIs, digits = 5)

##  source emmean       SE df lower.CL upper.CL CI.width
##  fish   3.3945 0.036681 23   3.3186   3.4704  0.15176
##  soy    3.6673 0.037448 23   3.5898   3.7447  0.15493
##  skim   3.7968 0.039383 23   3.7153   3.8782  0.16294
## 
## Results are averaged over the levels of: percent 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95

Back to contents

Adding grouping factors

Sometimes, users want to group levels of a factor into a smaller number of groups. Those groups may then be, say, averaged separately and compared, or used as a by factor. The add_grouping() function serves this purpose. The function takes four arguments: the object, the name of the grouping factor to be created, the name of the reference factor that is being grouped, and a vector of level names of the grouping factor corresponding to levels of the reference factor. Suppose for example that we want to distinguish animal and non-animal sources of protein in the pigs example:

pigs.emm.ss <- add_grouping(pigs.emm.s, "type", "source",
                            c("animal", "vegetable", "animal"))
str(pigs.emm.ss)

## 'emmGrid' object with variables:
##     source = fish, soy, skim
##     type = animal, vegetable
## Nesting structure:  source %in% type
## Transformation: "log"

Note that the new object has a nesting structure (see more about this in the “messy-data” vignette), with the reference factor nested in the new grouping factor. Now we can obtain means and comparisons for each group

emmeans(pigs.emm.ss, pairwise ~ type)

## $emmeans
##  type      emmean     SE df  n lower.CL upper.CL
##  animal      3.60 0.0267 23 19     3.54     3.65
##  vegetable   3.67 0.0374 23 10     3.59     3.74
## 
## Results are averaged over the levels of: percent, source 
## Results are given on the log (not the response) scale. 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast           estimate     SE df t.ratio p.value
##  animal - vegetable  -0.0716 0.0455 23  -1.573  0.1295
## 
## Results are averaged over the levels of: percent, source 
## Results are given on the log (not the response) scale.

Back to contents

Re-labeling or re-leveling an emmGrid

Sometimes it is desirable to re-label the rows of an emmGrid, or cast it in terms of other factor(s). This can be done via the levels argument in update().

As an example, sometimes a fitted model has a treatment factor that comprises combinations of other factors. In subsequent analysis, we may well want to break it down into the individual factors’ contributions. Consider, for example, the warpbreaks data provided with R. We will define a single factor and fit a non homogeneous-variance model:

warp <- transform(warpbreaks, treat = interaction(wool, tension))
library(nlme)
warp.gls <- gls(breaks ~ treat, weights = varIdent(form = ~ 1|treat), data = warp)
( warp.emm <- emmeans(warp.gls, "treat") )

##  treat emmean   SE   df lower.CL upper.CL
##  A.L     44.6 6.03 8.02     30.7     58.5
##  B.L     28.2 3.29 8.00     20.6     35.8
##  A.M     24.0 2.89 8.00     17.3     30.7
##  B.M     28.8 3.14 8.00     21.5     36.0
##  A.H     24.6 3.42 8.00     16.7     32.5
##  B.H     18.8 1.63 8.00     15.0     22.5
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

But now we want to re-cast this emmGrid into one that has separate factors for wool and tension. We can do this as follows:

warp.fac <- update(warp.emm, levels = list(
                wool = c("A", "B"), tension = c("L", "M", "H")))
str(warp.fac)

## 'emmGrid' object with variables:
##     wool = A, B
##     tension = L, M, H

So now we can do various contrasts involving the separate factors:

contrast(warp.fac, "consec", by = "wool")

## wool = A:
##  contrast estimate   SE   df t.ratio p.value
##  M - L     -20.556 6.69 11.5  -3.074  0.0203
##  H - M       0.556 4.48 15.6   0.124  0.9899
## 
## wool = B:
##  contrast estimate   SE   df t.ratio p.value
##  M - L       0.556 4.55 16.0   0.122  0.9881
##  H - M     -10.000 3.54 12.0  -2.824  0.0269
## 
## Degrees-of-freedom method: satterthwaite 
## P value adjustment: mvt method for 2 tests

Note: When re-leveling to more than one factor, you have to be careful to anticipate that the levels will be expanded using expand.grid(): the first factor in the list varies the fastest and the last varies the slowest. That was the case in our example, but in others, it may not be. Had the levels of treat been ordered as A.L, A.M, A.H, B.L, B.M, B.H, then we would have had to specify the levels of tension first and the levels of wool second.

Back to contents

Index of all vignette topics